2025-11-29
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This presentation summarizes:
Last Gift Cohort
Body Donation
Autopsy
Tissue Collection
HIV Sequencing
Phylogenetics and Modeling
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Note
Conceptual motivation
In a transition model, exposure represents the “opportunity” for observing a migration event between compartments. We considered the following definition:
\[ n_{\text{states}, i} : \text{number of Markov states in run } i \times \text{number of sequences from CNS} \times \text{number of sequences from Periphery} \]
data %>%
mutate(
pairs_cns = ntissues_cns * (ntissues_cns - 1) / 2,
pairs_periph = ntissues_periph * (ntissues_periph - 1) / 2,
exposure = case_when(
migration_type == "cross_BBB" & direction == "CNS to Periph" ~
n_states * nseq_cns * nseq_periph,
migration_type == "cross_BBB" & direction == "Periphery to CNS" ~
n_states * nseq_periph * nseq_cns,
migration_type == "within_CNS" ~
n_states * pairs_cns * (nseq_cns^2),
migration_type == "within_peripheral" ~
n_states * pairs_periph * (nseq_periph^2)
)
)
Notes
\[ \log(\mu_i) = \beta_0 + \beta_1 \cdot \text{Marker}_i + b_{\text{pid}(i)} + \log(\text{Exposure}_i) \]
fit_models <- function(marker, data_in) {
formula <- as.formula(
paste0(
"n_events ~ ", marker,
" + age + sex + last_cd4_t_cell_count + duration_infection_years +
(1 | pid) + offset(log_exposure)"
)
)
glmmTMB(
formula,
data = data_in,
family = nbinom2(),
control = glmmTMBControl(
optimizer = optim,
optArgs = list(method = "BFGS", maxit = 5000)
)
)
}
| Negative Binomial Models | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Associations between CSF biomarkers and counts of migration events from the CNS to the periphery | |||||||||
| Marker | Samples Detectable1 | Effect | Status | exp(Beta) | 95% CI | p-value | Significance | Direction | |
| Eotaxin (CCL11) | 13 | Ok | ✅ | 1.035 | 0.7967–1.35 | 0.795 | ⬆ | ||
| GM-CSF | 8 | Ok | ✅ | 0.884 | 0.7446–1.05 | 0.161 | ⬇ | ||
| GRO-alpha (CXCL1) | 13 | Ok | ✅ | 0.988 | 0.9808–1.00 | 0.001 | ** | ⬇ | |
| IL-1α | 1 | Sparse Data | ❌ | 0.160 | 0.0022–11.58 | 0.401 | ⬇ | ||
| IL-1β | 5 | Ok | ✅ | 0.806 | 0.6734–0.96 | 0.018 | * | ⬇ | |
| IL-1RA | 12 | Ok | ✅ | 1.000 | 0.9995–1.00 | 0.000 | *** | ⬇ | |
| IL-2 | 2 | Sparse Data | ❌ | 0.418 | 0.2951–0.59 | 0.000 | *** | ⬇ | |
| IL-5 | 5 | Ok | ✅ | 0.985 | 0.8082–1.20 | 0.885 | ⬇ | ||
| IL-6 | 12 | Ok | ✅ | 0.998 | 0.9971–1.00 | 0.000 | *** | ⬇ | |
| IL-7 | 13 | Ok | ✅ | 0.356 | 0.0618–2.05 | 0.248 | ⬇ | ||
| IL-8 (CXCL8) | 13 | Ok | ✅ | 0.998 | 0.9952–1.00 | 0.043 | * | ⬇ | |
| IL-9 | 1 | Sparse Data | ❌ | 1.104 | 0.5813–2.10 | 0.762 | ⬆ | ||
| IL-10 | 8 | Ok | ✅ | 0.481 | 0.2551–0.91 | 0.023 | * | ⬇ | |
| IL-15 | 5 | Ok | ✅ | 0.442 | 0.1617–1.21 | 0.112 | ⬇ | ||
| IL-17A/CTLA-8 | 1 | Sparse Data | ❌ | 0.409 | 0.0534–3.13 | 0.389 | ⬇ | ||
| IL-18 | 13 | Ok | ✅ | 1.009 | 0.9871–1.03 | 0.435 | ⬆ | ||
| IL-31 | 1 | Sparse Data | ❌ | 0.932 | 0.6748–1.29 | 0.668 | ⬇ | ||
| IP-10 (CXCL10) | 13 | Ok | ✅ | 1.000 | 0.9967–1.00 | 0.841 | ⬇ | ||
| MCP-1 (CCL2) | 13 | Ok | ✅ | 1.000 | 0.9985–1.00 | 0.908 | ⬇ | ||
| MIP-1α (CCL3) | 13 | Ok | ✅ | 0.868 | 0.7918–0.95 | 0.002 | ** | ⬇ | |
| MIP-1β (CCL4) | 13 | Ok | ✅ | 0.964 | 0.9420–0.99 | 0.002 | ** | ⬇ | |
| RANTES (CCL5) | 13 | Ok | ✅ | 1.007 | 0.9176–1.11 | 0.881 | ⬆ | ||
| SDF-1α | 13 | Ok | ✅ | 1.000 | 0.9993–1.00 | 0.798 | ⬆ | ||
| TNF-α | 2 | Sparse Data | ❌ | 0.328 | 0.2148–0.50 | 0.000 | *** | ⬇ | |
| 1 Number of samples with ≥1 measure(s) above the LOD across the 3 replicates. | |||||||||
| Negative Binomial Models | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Associations between CSF biomarkers and counts of migration events from the periphery to the CNS | |||||||||
| Marker | Samples Detectable1 | Effect | Status | exp(Beta) | 95% CI | p-value | Significance | Direction | |
| Eotaxin (CCL11) | 16 | Ok | ✅ | 1.002 | 0.875–1.15 | 0.972 | ⬆ | ||
| GM-CSF | 11 | Ok | ✅ | 0.849 | 0.752–0.96 | 0.008 | ** | ⬇ | |
| GRO-alpha (CXCL1) | 16 | Ok | ✅ | 0.987 | 0.980–0.99 | 0.000 | *** | ⬇ | |
| IL-1α | 1 | Sparse Data | ❌ | 0.258 | 0.011–6.02 | 0.399 | ⬇ | ||
| IL-1β | 7 | Ok | ✅ | 0.843 | 0.721–0.99 | 0.034 | * | ⬇ | |
| IL-1RA | 15 | Ok | ✅ | 1.000 | 1.000–1.00 | 0.007 | ** | ⬇ | |
| IL-2 | 2 | Sparse Data | ❌ | 0.374 | 0.283–0.49 | 0.000 | *** | ⬇ | |
| IL-5 | 7 | Ok | ✅ | 0.893 | 0.678–1.18 | 0.420 | ⬇ | ||
| IL-6 | 15 | Ok | ✅ | 0.998 | 0.997–1.00 | 0.000 | *** | ⬇ | |
| IL-7 | 16 | Ok | ✅ | 1.460 | 0.378–5.64 | 0.583 | ⬆ | ||
| IL-8 (CXCL8) | 16 | Ok | ✅ | 0.999 | 0.998–1.00 | 0.140 | ⬇ | ||
| IL-9 | 1 | Sparse Data | ❌ | 1.331 | 0.731–2.42 | 0.349 | ⬆ | ||
| IL-10 | 10 | Ok | ✅ | 0.823 | 0.685–0.99 | 0.038 | * | ⬇ | |
| IL-15 | 7 | Ok | ✅ | 0.249 | 0.119–0.52 | 0.000 | *** | ⬇ | |
| IL-17A/CTLA-8 | 2 | Sparse Data | ❌ | 0.745 | 0.250–2.22 | 0.596 | ⬇ | ||
| IL-18 | 16 | Ok | ✅ | 1.008 | 0.991–1.03 | 0.359 | ⬆ | ||
| IL-31 | 1 | Sparse Data | ❌ | 0.911 | 0.689–1.20 | 0.509 | ⬇ | ||
| IP-10 (CXCL10) | 16 | Ok | ✅ | 1.000 | 0.999–1.00 | 0.746 | ⬇ | ||
| MCP-1 (CCL2) | 16 | Ok | ✅ | 1.000 | 0.999–1.00 | 0.987 | ⬇ | ||
| MIP-1α (CCL3) | 16 | Ok | ✅ | 0.903 | 0.827–0.99 | 0.024 | * | ⬇ | |
| MIP-1β (CCL4) | 16 | Ok | ✅ | 0.987 | 0.969–1.01 | 0.157 | ⬇ | ||
| RANTES (CCL5) | 16 | Ok | ✅ | 1.001 | 0.935–1.07 | 0.987 | ⬆ | ||
| SDF-1α | 16 | Ok | ✅ | 1.000 | 0.999–1.00 | 0.296 | ⬇ | ||
| TNF-α | 3 | Sparse Data | ❌ | 0.380 | 0.220–0.66 | 0.001 | *** | ⬇ | |
| 1 Number of samples with ≥1 measure(s) above the LOD across the 3 replicates. | |||||||||
| Negative Binomial Models | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Associations between CSF biomarkers and counts of migration events across the BBB | |||||||||
| Marker | Samples Detectable1 | Effect | Status | exp(Beta) | 95% CI | p-value | Significance | Direction | |
| Eotaxin (CCL11) | 16 | Ok | ✅ | 1.079 | 0.9085–1.28 | 0.387 | ⬆ | ||
| GM-CSF | 11 | Ok | ✅ | 0.853 | 0.7522–0.97 | 0.013 | * | ⬇ | |
| GRO-alpha (CXCL1) | 16 | Ok | ✅ | 0.989 | 0.9803–1.00 | 0.007 | ** | ⬇ | |
| IL-1α | 1 | Sparse Data | ❌ | 0.295 | 0.0061–14.32 | 0.538 | ⬇ | ||
| IL-1β | 7 | Ok | ✅ | 0.824 | 0.6595–1.03 | 0.089 | . | ⬇ | |
| IL-1RA | 15 | Ok | ✅ | 1.000 | 0.9995–1.00 | 0.000 | *** | ⬇ | |
| IL-2 | 2 | Sparse Data | ❌ | 0.385 | 0.3005–0.49 | 0.000 | *** | ⬇ | |
| IL-5 | 7 | Ok | ✅ | 0.913 | 0.7685–1.08 | 0.300 | ⬇ | ||
| IL-6 | 15 | Ok | ✅ | 0.998 | 0.9973–1.00 | 0.000 | *** | ⬇ | |
| IL-7 | 16 | Ok | ✅ | 1.273 | 0.2751–5.89 | 0.757 | ⬆ | ||
| IL-8 (CXCL8) | 16 | Ok | ✅ | 0.999 | 0.9970–1.00 | 0.241 | ⬇ | ||
| IL-9 | 1 | Sparse Data | ❌ | 1.235 | 0.7163–2.13 | 0.448 | ⬆ | ||
| IL-10 | 10 | Ok | ✅ | 0.794 | 0.6189–1.02 | 0.069 | . | ⬇ | |
| IL-15 | 7 | Ok | ✅ | 0.286 | 0.1314–0.62 | 0.002 | ** | ⬇ | |
| IL-17A/CTLA-8 | 2 | Sparse Data | ❌ | 1.011 | 0.2677–3.82 | 0.987 | ⬆ | ||
| IL-18 | 16 | Ok | ✅ | 1.010 | 0.9911–1.03 | 0.313 | ⬆ | ||
| IL-31 | 1 | Sparse Data | ❌ | 0.955 | 0.7104–1.28 | 0.762 | ⬇ | ||
| IP-10 (CXCL10) | 16 | Ok | ✅ | 1.000 | 0.9988–1.00 | 0.576 | ⬆ | ||
| MCP-1 (CCL2) | 16 | Ok | ✅ | 1.000 | 0.9987–1.00 | 0.763 | ⬇ | ||
| MIP-1α (CCL3) | 16 | Ok | ✅ | 0.883 | 0.7876–0.99 | 0.032 | * | ⬇ | |
| MIP-1β (CCL4) | 16 | Ok | ✅ | 0.988 | 0.9626–1.01 | 0.343 | ⬇ | ||
| RANTES (CCL5) | 16 | Ok | ✅ | 1.024 | 0.9410–1.11 | 0.581 | ⬆ | ||
| SDF-1α | 16 | Ok | ✅ | 1.000 | 0.9976–1.00 | 0.996 | ⬇ | ||
| TNF-α | 3 | Sparse Data | ❌ | 0.358 | 0.2383–0.54 | 0.000 | *** | ⬇ | |
| 1 Number of samples with ≥1 measure(s) above the LOD across the 3 replicates. | |||||||||
| Negative Binomial Models (per transition) | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Associations between CSF biomarkers and counts of migration events from the CNS to the periphery | |||||||||
| Marker | Samples Detectable1 | Effect | Status | exp(Beta) | 95% CI | p-value | Significance | Direction | |
| Eotaxin (CCL11) | 13 | Ok | ✅ | 1.060 | 0.930–1.21 | 0.385 | ⬆ | ||
| GM-CSF | 8 | Ok | ✅ | 0.939 | 0.863–1.02 | 0.140 | ⬇ | ||
| GRO-alpha (CXCL1) | 13 | Ok | ✅ | 0.996 | 0.992–1.00 | 0.090 | . | ⬇ | |
| IL-1α | 1 | Sparse Data | ❌ | 0.248 | 0.032–1.92 | 0.181 | ⬇ | ||
| IL-1β | 5 | Ok | ✅ | 0.986 | 0.856–1.13 | 0.840 | ⬇ | ||
| IL-1RA | 12 | Ok | ✅ | 1.000 | 1.000–1.00 | 0.001 | *** | ⬇ | |
| IL-2 | 2 | Sparse Data | ❌ | 0.668 | 0.515–0.87 | 0.002 | ** | ⬇ | |
| IL-5 | 5 | Ok | ✅ | 0.997 | 0.904–1.10 | 0.954 | ⬇ | ||
| IL-6 | 12 | Ok | ✅ | 0.999 | 0.999–1.00 | 0.068 | . | ⬇ | |
| IL-7 | 13 | Ok | ✅ | 1.570 | 0.581–4.24 | 0.373 | ⬆ | ||
| IL-8 (CXCL8) | 13 | Ok | ✅ | 0.999 | 0.998–1.00 | 0.033 | * | ⬇ | |
| IL-9 | 1 | Sparse Data | ❌ | 0.916 | 0.662–1.27 | 0.595 | ⬇ | ||
| IL-10 | 8 | Ok | ✅ | 0.648 | 0.478–0.88 | 0.005 | ** | ⬇ | |
| IL-15 | 5 | Ok | ✅ | 0.666 | 0.398–1.11 | 0.121 | ⬇ | ||
| IL-17A/CTLA-8 | 1 | Sparse Data | ❌ | 0.555 | 0.203–1.52 | 0.252 | ⬇ | ||
| IL-18 | 13 | Ok | ✅ | 1.009 | 0.998–1.02 | 0.098 | . | ⬆ | |
| IL-31 | 1 | Sparse Data | ❌ | 1.044 | 0.886–1.23 | 0.609 | ⬆ | ||
| IP-10 (CXCL10) | 13 | Ok | ✅ | 1.000 | 0.999–1.00 | 0.832 | ⬆ | ||
| MCP-1 (CCL2) | 13 | Ok | ✅ | 1.000 | 0.999–1.00 | 0.451 | ⬇ | ||
| MIP-1α (CCL3) | 13 | Ok | ✅ | 0.963 | 0.892–1.04 | 0.340 | ⬇ | ||
| MIP-1β (CCL4) | 13 | Ok | ✅ | 0.985 | 0.969–1.00 | 0.067 | . | ⬇ | |
| RANTES (CCL5) | 13 | Ok | ✅ | 1.009 | 0.963–1.06 | 0.699 | ⬆ | ||
| SDF-1α | 13 | Ok | ✅ | 1.000 | 1.000–1.00 | 0.897 | ⬆ | ||
| TNF-α | 2 | Sparse Data | ❌ | 0.593 | 0.436–0.81 | 0.001 | *** | ⬇ | |
| 1 Number of samples with ≥1 measure(s) above the LOD across the 3 replicates. | |||||||||
| Negative Binomial Models (per transition) | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Associations between CSF biomarkers and counts of migration events from the periphery to the CNS | |||||||||
| Marker | Samples Detectable1 | Effect | Status | exp(Beta) | 95% CI | p-value | Significance | Direction | |
| Eotaxin (CCL11) | 16 | Ok | ✅ | 1.017 | 0.927–1.12 | 0.716 | ⬆ | ||
| GM-CSF | 11 | Ok | ✅ | 0.910 | 0.862–0.96 | 0.001 | *** | ⬇ | |
| GRO-alpha (CXCL1) | 16 | Ok | ✅ | 0.995 | 0.991–1.00 | 0.002 | ** | ⬇ | |
| IL-1α | 1 | Sparse Data | ❌ | 0.622 | 0.085–4.52 | 0.639 | ⬇ | ||
| IL-1β | 7 | Ok | ✅ | 0.998 | 0.879–1.13 | 0.970 | ⬇ | ||
| IL-1RA | 15 | Ok | ✅ | 1.000 | 1.000–1.00 | 0.001 | ** | ⬇ | |
| IL-2 | 2 | Sparse Data | ❌ | 0.645 | 0.539–0.77 | 0.000 | *** | ⬇ | |
| IL-5 | 7 | Ok | ✅ | 0.963 | 0.886–1.05 | 0.376 | ⬇ | ||
| IL-6 | 15 | Ok | ✅ | 0.999 | 0.999–1.00 | 0.003 | ** | ⬇ | |
| IL-7 | 16 | Ok | ✅ | 1.645 | 0.746–3.63 | 0.217 | ⬆ | ||
| IL-8 (CXCL8) | 16 | Ok | ✅ | 0.999 | 0.998–1.00 | 0.109 | ⬇ | ||
| IL-9 | 1 | Sparse Data | ❌ | 1.007 | 0.761–1.33 | 0.960 | ⬆ | ||
| IL-10 | 10 | Ok | ✅ | 0.862 | 0.748–0.99 | 0.042 | * | ⬇ | |
| IL-15 | 7 | Ok | ✅ | 0.530 | 0.378–0.74 | 0.000 | *** | ⬇ | |
| IL-17A/CTLA-8 | 2 | Sparse Data | ❌ | 0.872 | 0.436–1.74 | 0.697 | ⬇ | ||
| IL-18 | 16 | Ok | ✅ | 1.012 | 1.004–1.02 | 0.005 | ** | ⬆ | |
| IL-31 | 1 | Sparse Data | ❌ | 1.094 | 0.950–1.26 | 0.213 | ⬆ | ||
| IP-10 (CXCL10) | 16 | Ok | ✅ | 1.000 | 0.999–1.00 | 0.973 | ⬇ | ||
| MCP-1 (CCL2) | 16 | Ok | ✅ | 1.000 | 0.999–1.00 | 0.627 | ⬇ | ||
| MIP-1α (CCL3) | 16 | Ok | ✅ | 0.978 | 0.911–1.05 | 0.532 | ⬇ | ||
| MIP-1β (CCL4) | 16 | Ok | ✅ | 0.993 | 0.979–1.01 | 0.291 | ⬇ | ||
| RANTES (CCL5) | 16 | Ok | ✅ | 1.001 | 0.961–1.04 | 0.945 | ⬆ | ||
| SDF-1α | 16 | Ok | ✅ | 1.000 | 0.999–1.00 | 0.885 | ⬇ | ||
| TNF-α | 3 | Sparse Data | ❌ | 0.573 | 0.458–0.72 | 0.000 | *** | ⬇ | |
| 1 Number of samples with ≥1 measure(s) above the LOD across the 3 replicates. | |||||||||
| Negative Binomial Models (per transition) | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Associations between CSF biomarkers and counts of migration events across the BBB | |||||||||
| Marker | Samples Detectable1 | Effect | Status | exp(Beta) | 95% CI | p-value | Significance | Direction | |
| Eotaxin (CCL11) | 16 | Ok | ✅ | 1.022 | 0.933–1.12 | 0.638 | ⬆ | ||
| GM-CSF | 11 | Ok | ✅ | 0.917 | 0.864–0.97 | 0.004 | ** | ⬇ | |
| GRO-alpha (CXCL1) | 16 | Ok | ✅ | 0.995 | 0.991–1.00 | 0.004 | ** | ⬇ | |
| IL-1α | 1 | Sparse Data | ❌ | 0.418 | 0.062–2.82 | 0.370 | ⬇ | ||
| IL-1β | 7 | Ok | ✅ | 0.983 | 0.867–1.11 | 0.782 | ⬇ | ||
| IL-1RA | 15 | Ok | ✅ | 1.000 | 1.000–1.00 | 0.000 | *** | ⬇ | |
| IL-2 | 2 | Sparse Data | ❌ | 0.643 | 0.539–0.77 | 0.000 | *** | ⬇ | |
| IL-5 | 7 | Ok | ✅ | 0.966 | 0.889–1.05 | 0.411 | ⬇ | ||
| IL-6 | 15 | Ok | ✅ | 0.999 | 0.999–1.00 | 0.003 | ** | ⬇ | |
| IL-7 | 16 | Ok | ✅ | 1.580 | 0.722–3.46 | 0.252 | ⬆ | ||
| IL-8 (CXCL8) | 16 | Ok | ✅ | 0.999 | 0.998–1.00 | 0.050 | * | ⬇ | |
| IL-9 | 1 | Sparse Data | ❌ | 0.980 | 0.744–1.29 | 0.884 | ⬇ | ||
| IL-10 | 10 | Ok | ✅ | 0.867 | 0.752–1.00 | 0.048 | * | ⬇ | |
| IL-15 | 7 | Ok | ✅ | 0.543 | 0.379–0.78 | 0.001 | *** | ⬇ | |
| IL-17A/CTLA-8 | 2 | Sparse Data | ❌ | 0.863 | 0.432–1.72 | 0.677 | ⬇ | ||
| IL-18 | 16 | Ok | ✅ | 1.011 | 1.003–1.02 | 0.005 | ** | ⬆ | |
| IL-31 | 1 | Sparse Data | ❌ | 1.074 | 0.931–1.24 | 0.329 | ⬆ | ||
| IP-10 (CXCL10) | 16 | Ok | ✅ | 1.000 | 0.999–1.00 | 0.965 | ⬆ | ||
| MCP-1 (CCL2) | 16 | Ok | ✅ | 1.000 | 0.999–1.00 | 0.482 | ⬇ | ||
| MIP-1α (CCL3) | 16 | Ok | ✅ | 0.968 | 0.904–1.04 | 0.350 | ⬇ | ||
| MIP-1β (CCL4) | 16 | Ok | ✅ | 0.991 | 0.978–1.00 | 0.206 | ⬇ | ||
| RANTES (CCL5) | 16 | Ok | ✅ | 1.005 | 0.964–1.05 | 0.810 | ⬆ | ||
| SDF-1α | 16 | Ok | ✅ | 1.000 | 1.000–1.00 | 0.953 | ⬇ | ||
| TNF-α | 3 | Sparse Data | ❌ | 0.576 | 0.464–0.72 | 0.000 | *** | ⬇ | |
| 1 Number of samples with ≥1 measure(s) above the LOD across the 3 replicates. | |||||||||
HIV Persistence & Dynamics | Research Presentation